Evaluating Quality of Segmentation of an Image into Different Types of Tissue for Planning Treatment Using Tumor Treating Fields (TTFields)

ABSTRACT

To plan tumor treating fields (TTFields) therapy, a model of a patient&#39;s head is often used to determine where to position the transducer arrays during treatment, and the accuracy of this model depends in large part on an accurate segmentation of MRI images. The quality of a segmentation can be improved by presenting the segmentation to a previously-trained machine learning system. The machine learning system generates a quality score for the segmentation. Revisions to the segmentation are accepted, and the machine learning system scores the revised segmentation. The quality scores are used to determine which segmentation provides better results, optionally by running simulations for models that correspond to each segmentation for a plurality of different transducer array layouts.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 16/736,604, filed Jan. 7, 2020, which claims the benefit of U.S. Provisional Application 62/789,660, filed Jan. 8, 2019, each of which is incorporated herein by reference in its entirety.

BACKGROUND

Tumor treating fields (TTFields) is an FDA approved therapy for the management of Glioblastoma Multiform (GBM) and under study for multiple additional indications. See, e.g., Stupp, R., et al.: Effect of Tumor-Treating Fields Plus Maintenance Temozolomide vs Maintenance Temozolomide Alone on Survival in Patients With Glioblastoma: A Randomized Clinical Trial. JAMA. 318, 2306-2316 (2017), which is incorporated herein by reference. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient's scalp and delivery of an electric field to the tumor area. Larger electric field intensities or power densities within the tumor are associated with improved treatment outcomes. Both of these parameters directly depend on the location of the TAs.

One approach for determining where to place the TAs on a particular patient's head is to create an accurate model of the patient's head that includes a tissue-type (e.g., white matter, grey matter, CSF, etc.) for each voxel in the image; position simulated TAs on the model; apply simulated voltages to the TAs; and compute the electric field or power density at each tumor voxel in the model. But heretofore, the step of creating the accurate model of the patient's head that includes the tissue type for each voxel has been an extremely labor-intensive process.

Automated and semi-automated approaches for creating models of a patient's head exist, and these approaches are less labor-intensive. But the accuracy/quality of the models generated using these approaches can vary dramatically from image to image. And when a model with poor accuracy/quality is used to compute the electric field or power density at each tumor voxel in the model, the calculated electric field or power density can diverge significantly with respect to the actual electric field or power density. And this divergence can have clinical implications because when the actual electric field or power density is significantly lower than predicted, the patient may not receive the full benefit of his or her TTFields treatment.

SUMMARY OF THE INVENTION

One aspect of the invention is directed to a first method of estimating a quality of a segmentation of an image. The first method comprises training a machine learning system to estimate quality of a segmentation of an image based on a plurality of reference images and at least one quality score that has been assigned to each of the reference images. The first method also comprises presenting a new image and a segmentation of the new image to the machine learning system; receiving, from the machine learning system, at least one first quality score for the segmentation of the new image; and outputting the at least one first quality score for the segmentation of the new image.

In some instances of the first method, the machine learning system is trained to estimate the quality of segmentation of an image based on (a) a quality of affine registration, (b) a quality of deformable registration, (c) input image properties, and (d) geometrical properties of segmented tissues.

In some instances of the first method, the quality of deformable registration is determined based on a deformation's field bias, directional variability, and mean per-axis variability. In some instances of the first method, the input image properties comprise a shortest axis length and a signal to noise ratio of each tissue separately. In some instances of the first method, the geometrical properties of the segmented tissues comprise a volume of a shape and a number of connected components, each of which is computed per tissue.

In some instances of the first method, the machine learning system is trained to estimate the quality of segmentation of the new image based on at least one global quality feature, at least one local quality feature, and a shortest axis length for intra-cranial tissues. In these instances, the machine learning system is trained to estimate the quality of segmentation of the new image based on image quality and tissue's shape properties for extra-cranial tissues.

Some instances of the first method further comprise making an automatic adjustment to the segmentation; presenting the adjusted segmentation to the machine learning system; receiving, from the machine learning system, at least one second quality score for the adjusted segmentation; and outputting an indication when the at least one second quality score indicates an improved quality with respect to the at least one first quality score.

Another aspect of the invention is directed to a second method of improving quality of a segmentation of an image. The second method comprises presenting a new image and a first segmentation of the new image to a machine learning system. The machine learning system has been trained to estimate quality of a segmentation of an image based on a plurality of reference images and at least one quality score that has been assigned to each of the reference images. The second method also comprises receiving, from the machine learning system, at least one first quality score for the first segmentation of the new image; outputting the at least one first quality score for the first segmentation of the new image; and accepting, from a user, at least one revision to the first segmentation. The second method also comprises presenting, to the machine learning system, a second segmentation of the new image, where the second segmentation is based on the at least one revision to the first segmentation; receiving, from the machine learning system, at least one second quality score for the second segmentation of the new image; and outputting the at least one second quality score for the second segmentation of the new image.

In some instances of the second method, the at least one first quality score for the first segmentation of the new image consists of a single first quality score for an entirety of the new image, and the at least one second quality score for the second segmentation of the new image consists of a single second quality score for an entirety of the new image.

In some instances of the second method, the at least one first quality score for the first segmentation of the new image comprises a separate first quality score for each of a plurality of regions within the new image, and the at least one second quality score for the second segmentation of the new image comprises a separate second quality score for each of a plurality of regions within the new image.

In some instances of the second method, the at least one first quality score for the first segmentation of the new image comprises a separate first quality score for each of a plurality of tissue types within the new image, and the at least one second quality score for the second segmentation of the new image comprises a separate second quality score for each of a plurality of tissue types within the new image.

In some instances of the second method, the at least one revision to the first segmentation comprise an adjustment that shifts a calculated probability that a given voxel belongs to a particular tissue type.

In some instances of the second method, the at least one revision to the first segmentation comprise an adjustment that, for all voxels in the new image that correspond to tissue, (a) increases a calculated probability that a given voxel belongs to a first tissue type and (b) decreases a calculated probability that a given voxel belongs to a second tissue type.

Some instances of the second method further comprise (a) calculating how a determined quality of the first segmentation can change an average expected power density in a target region of the new image that corresponds to a tumor by running a finite element simulation using tissue types selected based on the first segmentation, and (b) calculating how a determined quality of the second segmentation can change an average expected power density in the target region of the new image by running a finite element simulation using tissue types selected based on the second segmentation.

Some instances of the second method further comprise (a) calculating how a determined quality of the first segmentation can change a lowest reasonably expected power density in a target region of the new image that corresponds to a tumor by running a finite element simulation using tissue types selected based on the first segmentation, and (b) calculating how a determined quality of the second segmentation can change a lowest reasonably expected power density in the target region of the new image by running a finite element simulation using tissue types selected based on the second segmentation.

Some instances of the second method further comprise maximizing the lowest reasonably expected power density in the tumor bed. Some instances of the second method further comprise computing a confidence score for at least one candidate layout, wherein the confidence score is indicative of odds that a given layout is the best layout.

Another aspect of the invention is directed to a third method of determining a quality of a segmentation. The third method comprises measuring the Dice coefficient between a computed head segmentation and a validated head segmentation of a training set; extracting features to account for a deformable registration quality; incorporating a shortest axis length and a signal to noise ratio of each tissue separately as input image quality; and comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions.

In some instances of the third method, the features extracted to account for the deformable registration quality comprise: deformation's field bias, directional variability, and mean per-axis variability.

In some instances of the third method, a volume of a shape and a number of connected components are used to describe a segmentation shape.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A depicts an MRI T1w image of a GBM patient's head.

FIG. 1B depicts the segmentation of the FIG. 1A MRI using a reference algorithm.

FIG. 1C depicts a validated segmentation that corresponds to the FIG. 1A MRI.

FIG. 2A depicts Absolute Pearson's correlation between computed features and the Dice coefficients of each segmented tissue.

FIG. 2B depicts the suggested features and a decision tree regressor output predictions of Dice coefficients.

FIG. 3 is a flowchart depicting a set of steps for using the quality-estimation machine learning system to improve planning of the positioning of transducer arrays for TTFields therapy.

FIG. 4 depicts an example of a suitable user interface that may be used to make the quality presentation to the user, and also to allow the user to make corrections to the segmentation.

FIGS. 5A-5C depict an example of a user interface that may be used to summarize the estimated field strength (or power density) in a tumor bed for five different candidate transducer array layouts.

Various embodiments are described in detail below with reference to the accompanying drawings, wherein like reference numerals represent like elements.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

A key step to improving the placement of TAs in order to obtain the desired field strength (or power density) in the tumor is to correctly segment the head into tissues of similar electrical properties. The conventional approach of visual inspection of segmentation quality is invaluable but time-consuming.

This application describes approaches for estimating the quality or accuracy of a model after the model has been generated. The model may be generated using a manual, automatic, or semi-automatic approach, or a combination of two or more of those approaches. Obtaining an estimate of the quality of a given model can be useful in a number of situations. In one example, when a particular model is tested and it turns out that the estimated quality of that model is low, this outcome lets the user know that steps should be taken to improve the quality of the model before proceeding to the TA placement/simulation stage. On the other hand, when a particular model is tested and it turns out that the estimated quality of that model is high, knowledge of this outcome can provide the operator with an indication that they have spent enough time refining the segmentation, and can proceed to the TA placement simulation stage.

Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user, and indicate if the segmented method is of sufficient accuracy for TTFields simulation.

FIG. 1A depicts an MRI T1w image of a GBM patient's head; FIG. 1B depicts the segmentation of the FIG. 1A MRI using a reference algorithm and; FIG. 1C depicts a validated segmentation that corresponds to the FIG. 1A MRI. The tumor was semi-automatically pre-segmented in both segmentations.

The embodiments described herein make it possible to predict the similarity between a segmentation generated by a given algorithm (e.g., the FIG. 1B segmentation) and an expected validated segmentation without ever generating that expected validated segmentation (i.e., a figure that, if generated, would correspond to FIG. 1C).

In one approach for accomplishing this goal, the inventors identified a set of features that are relevant to atlas-based segmentation and showed that these features are significantly correlated (p<0.05) with a similarity measure between validated and automatically computed segmentations. These features were incorporated in a decision tree regressor to predict the similarity of the validated and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average abs. difference 3% (SD=3%); r=0.92, p<0.001). It is therefore reasonable to conclude that quality estimation of segmentations is feasible by incorporating machine learning and segmentation-relevant features. Note that although only a single machine learning approach is described in this paragraph, a variety of alternative machine learning approaches may be substituted therefore, as will be appreciated by persons skilled in the relevant arts.

One example of a semi-automatic protocol for estimating the electric field within the tumor of a specific GBM patient for different TA layouts includes three steps: 1) head segmentation and the assignment of electrical tissue properties (conductivity and/or permittivity) to each voxel in the 3D image; 2) virtual placement of the TAs on the outer surface of the head, and; 3) simulation of the electric field propagation and estimation of the dose within the tumor. See, e.g., Bomzon, Z., et al.: Using computational phantoms to improve delivery of Tumor Treating Fields (TTFields) to patients: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 6461-6464. IEEE (2016), which is incorporated herein by reference. The head segmentation may be performed semi-automatically, for example by first using SPM-MARS and tweaking its parameters, and then by manually fixing the segmentation errors. See, e.g., Huang, Y., Parra, L. C.: Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity, with Theory Reviewed. PLoS One. 10, e0125477 (2015), which is incorporated herein by reference.

One approach that is suitable for performing the first step (head segmentation) is an atlas-based automatic head segmentation method. In one example, to ensure that estimates of TTFields dosage (e.g., in the tumor bed) remain similar and relevant to the outcomes, the inventors developed a process for estimating the quality of the atlas-based segmentation method. This method was specifically designed for evaluating atlas-based segmentation algorithms, aiming to facilitate a better estimation. Optionally, more general methods can also be incorporated for this purpose. See, e.g., Gerig, G., et al.: Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation. Presented at the Oct. 14 (2001); Warfield, S. K., et al.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging. 23, 903-21 (2004); and Commowick, O., et al.: Estimating a reference standard segmentation with spatially varying performance parameters: local MAP STAPLE. IEEE Trans. Med. Imaging. 31, 1593-606 (2012), each of which is incorporated herein by reference.

The method for estimating the quality of a given segmentation relies on predicting a similarity measure between computed segmentations and validated segmentations of the head, but in the absence of the validated segmentation. To measure the quality of the segmentation, the Dice coefficient was measured between the computed- and validated-head segmentations of a training set. Then, four categories of features that seem to be relevant for atlas based segmentation methods were investigated: 1) quality of global (affine) registration; 2) quality of local (deformable) registration; 3) input image properties, and; 4) geometrical properties of the segmented tissues.

In one example, global registration quality is estimated with inverse consistency. See, e.g., Rivest-Henault, et al.: Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy. Med. Image Anal. 23, 56-69 (2015), which is incorporated herein by reference. In this example, the following features are extracted to account for the deformable registration quality: 1) deformation's field bias (mean of all vectors); 2) directional variability (SD of the 3-element vector that is the mean of each axis), and; 3) mean per-axis variability (the mean of 3-element vector that is the SD of each axis). In this example, the shortest axis length and the signal to noise ratio of each tissue separately are used as indicators of input image quality. And although many features that describe the segmentation shape can be defined, two measures were selected in this example: the volume of the shape and the number of connected components. These measures were computed per tissue as depicted in FIG. 2A.

The features were incorporated in a decision tree regressor. See, e.g., Breiman, L.: Classification And Regression Trees. Routledge (2017), which is incorporated herein by reference. A leave-one-out approach was applied on 20 TTFields patients' head MR-T1 images, their validated segmentations, and their automatically generated counterparts. Finally, a comparison of the measured Dice coefficients between the sets to the Dice coefficients' predictions was made.

FIG. 2A depicts Absolute Pearson's correlation between computed features (columns) and the Dice coefficients of each segmented tissue (rows). The Dice coefficients were computed between the validated head segmentations and those that were computed with a new automatic segmentation method (* p<0.05).

FIG. 2B depicts the suggested features and a decision tree regressor output predictions of Dice coefficients that are in a high correlation with the actual ones (r=0.92; p<0.001). Note that in FIGS. 2A-2B, CSF—cerebrospinal fluid; Skin—skin and muscle; GM—grey matter; WM—white matter; SNR—signal to noise ratio; cc—# connected components. The results depicted in FIGS. 2A-2B show that intra-cranial tissues were significantly (p<0.05) correlated with the registration (global and local) quality features and the shortest axis length; and in contrast, extra-cranial structures were significantly (p<0.05) correlated with image quality and tissue's shape properties. Moreover, the predicted measures were similar and highly correlated with the actual ones (average absolute difference 3% (SD=3%); r=0.92, p<0.001).

The results described herein show that a segmentation's quality estimation is feasible by incorporating a machine learning approach and features that are relevant to the segmentation. Optionally, the simultaneous truth and performance level estimation (STAPLE) method or one of its variants may be incorporated to improve the prediction of segmentation quality. See, e.g., Akhondi-Asl, et al.: Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. IEEE Trans. Med. Imaging. 32, 1840-52 (2013), which is incorporated herein by reference, as well as the Warfield and Commowick references identified above. The segmentation quality estimation can improve the TTFields planning, and quantifying the effects of segmentation errors on the TTFields simulation results is described below.

Optionally, the system can be programmed to make an automatic adjustment to the segmentation, in order to arrive at an improved segmentation. For example, the system could change one or more parameters that have a global effect on the borders between certain tissue types. One example of such a change is to make an adjustment that, for all voxels in the image that correspond to tissue, (a) increases a calculated probability that a given voxel belongs to a first tissue type and (b) decreases a calculated probability that a given voxel belongs to a second tissue type.

After the automatic adjustment is made, the quality of the segmentation is reevaluated by presenting the adjusted segmentation to the machine learning system. The machine learning system outputs at least one second quality score for the adjusted segmentation. An indication is output when the at least one second quality score indicates an improved quality with respect to the at least one first quality score. This approach can be used to improve the segmentation by making automatic adjustments to the segmentation.

One common prior art approach for planning TTFields therapy involves the following four steps: create segmentation; user's visual evaluation of segmentation quality; segmentation correction using standard tools; and user's clinical decision making based on TTFields estimation.

FIG. 3 is a flowchart depicting a set of steps for using the quality-estimation machine learning system to improve planning of the positioning of transducer arrays for TTFields therapy. Step 10 is training a machine learning system to estimate quality of a segmentation, and this can be done based on a plurality of reference images and at least one quality score that has been assigned to each of the reference images, as described above in connection with FIGS. 1 and 2.

If we assume that the machine learning system has previously been trained in step 10, the first step of the process for any new image will be step 20. Steps 20-70 in FIG. 3 depict an example of a TTFields planning protocol that improves upon the prior art approach by incorporating an automatic segmentation quality estimation (e.g., the one described above in connection with FIGS. 1-2) and guidance regarding segmentation errors and TTFields computation quality. In step 20, the user creates a segmentation of the medical image (e.g., using computational methods or manually). Any of a wide variety of conventional approaches that will be apparent to persons skilled in the relevant art may be used for creating the initial segmentation. In step 30, this segmentation as well as the associated original image are presented to the machine learning system.

Next, in step 40, a quality estimate for the presented segmentation is obtained from the machine learning system (e.g., as described above in connection with FIG. 1-2) and this quality estimate is presented to the user.

FIG. 4 depicts an example of a suitable user interface that may be used to make the quality presentation to the user, and also to allow the user to make corrections to the segmentation. The quality presentation may be made in a variety of ways, including but not limited to (a) a single overall score number for any given segmentation (e.g., the 60% score depicted on the lower left of FIG. 4), (b) a recommendation that specific tissues should be revised due to a low quality score in any given segmentation (e.g., as depicted on the upper left of FIG. 4), and/or (c) an identification of particular regions on the image where the calculated quality of the segmentation is low for any given segmentation. One way to indicate the latter is by overlaying zones that are associated with low segmentation quality with a transparency (e.g., the circle 42 depicted in FIG. 4) or a region of opacity (e.g., the region 44 in FIG. 4).

A wide variety of alternative approaches for depicting zones that are associated with low segmentation quality will be readily apparent to persons skilled in the relevant arts, including but not limited to generating a color coded overlay (similar to a heat map) that depicts the estimated quality of the segmentation at each point in the image. In alternative embodiments, only a subset of the approaches depicted in FIG. 4 are provided to the user. In alternative embodiments, a different user interface is provided to the user to make the quality presentation.

In the next step (i.e., step 50) of the FIG. 3 protocol, the user revises the segmentation based on the quality data that was presented in step 40. This may be accomplished using a variety of approaches. In one approach (step 50A), the user executes an automatic correction method. In another approach (step 50B) the user changes parameters that have a global effect on the borders. This may be accomplished, for example, by making an adjustment that, for all voxels in the new image that correspond to tissue, (a) increases a calculated probability that a given voxel belongs to a first tissue type and (b) decreases a calculated probability that a given voxel belongs to a second tissue type. This adjustment may be made for any given segmentation using any appropriate user interface, including but not limited to the sliders depicted on the bottom of FIG. 4. For example, sliding the first slider 46 labeled “Skull to Muscles” to the left progressively increases the calculated probability that any given voxel is a skull voxel and progressively decreases the calculated probability that the given voxel is a muscle voxel. And sliding the first slider 46 to the right progressively increases the calculated probability that any given voxel is a muscle voxel and progressively decreases the calculated probability that the given voxel is a skull voxel. In yet another approach (step 50C), the user uses standard segmentation tools such as brush or polygon marking for correction of local errors. Note that the three approaches identified above are merely examples of how to revise the segmentation, and a variety of alternative approaches that will be apparent to persons skilled in the relevant arts may also be used.

In some preferred embodiments, after revisions to the segmentation have been made in step 50, the system recalculates the quality of segmentation in step 60 (e.g. using the approaches described above in connection with FIGS. 1 and 2) and presents the revised results to the user. This may be done in real time in response to adjustments that are made by the user in step 50. Alternatively, this may be done upon user request (e.g., by including a “refresh quality estimate” button on the user interface.

The next step (i.e., step 70) of the FIG. 3 protocol occurs after corrections to the segmentation have been made. In this step, the estimated quality for the segmentation that has been selected by the user is used to plan TTFields treatment, taking into account the quality estimate for the selected segmentation. The quality of the TTFields simulation is directly related to the quality of tissue segmentation, because assigning the wrong tissue can result in inaccurate estimation of the field and suboptimal TTFields treatment recommendation. The relation between segmentation and TTFields errors can be modeled such that an estimate of the segmentation quality will facilitate an estimate for the computed TTFields quality. TTFields quality estimates may be utilized to assist the caregiver in the treatment decision making. For example, assume that there are two recommended transducer array layouts with similar TTFields dose, but the TTFields quality is much better for one layout than the other. The embodiments described herein provide this information to the user so that they can make a better-informed clinical decision (e.g., by selecting the layout with the higher quality).

Taking the previous idea one step further, the error in segmentation can be propagated to effect the recommended treatment automatically. That is, the software will prefer setups in which the confidence regarding the TTFields dose is high in addition to other factors.

FIGS. 5A-5C depicts an example of a suitable user interface that may be used to summarize the estimated field strength (or power density) in the tumor bed for five different candidate transducer array layouts (TAL 1-TAL 5), and also show how the quality of the segmentation might impact the field-strength estimates. The operator can use this information as a basis for selecting a given candidate transducer array layout over the other candidates.

For each candidate transducer array layout based on a given segmentation, the system calculates how a determined quality of the given segmentation can lower an average expected field strength in a target region (e.g., the region that corresponds to a tumor) by running a finite element simulation using tissue types selected based on the given segmentation. In addition, for each candidate transducer array layout based on a given segmentation, the system calculates how a determined quality of the given segmentation can lower a minimum expected field strength in a target region by running a finite element simulation using tissue types selected based on the given segmentation.

More specifically, FIG. 5A shows a table that presents two values for each of the candidate transducer array layouts being considered (TAL 1-TAL 5). For each transducer array layout, the value on the left is the average estimated TTFields power density in the tumor when the quality of the segmentation is ignored; and the value on the right is the lowest (or 5^(th) percentile) average estimated TTFields power density in the tumor when the quality of the segmentation is considered.

In this example, the first candidate transducer array layout (TAL1) is associated with the highest average estimated TTFields power density in the tumor of 80 mW/cm². But when the impact of possible errors in the segmentation is considered, the TTFields power density for that same layout (TAL1) could be as low as 30 mW/cm². In contrast, the third candidate transducer array layout (TAL3) has a lower average estimated TTFields power density in the tumor when possible error in segmentation are ignored (63 mW/cm², see the left column for TAL3). But when the impact of possible errors in the segmentation is considered, the lowest reasonably expected (e.g., 5^(th) percentile) power density for the TAL3 layout will also be 63 mW/cm² (right column for TAL3). Because the quality of the segmentation will not have as big a negative impact on the field power density when the TAL3 layout is selected, and because the lowest reasonably expected power density for that layout (i.e. 63 mW/cm²) is significantly higher than the lowest reasonably expected power density for the TAL1 layout (30 mW/cm²), the TAL3 layout is preferable to the TAL1 layout, and should therefore be selected.

In other words, determining which TTFields simulation is the best can be accomplished by maximizing X, where X is the lowest reasonably expected power density in the tumor bed. Optionally, the system can be programmed to calculate which layout maximizes X, and to recommend that layout. Optionally, a confidence score may be associated with one or more possible layouts, where the confidence score is indicative of odds that a given layout is the best layout.

Optionally, the effect of the TTFields spatial distribution for each of the candidate transducer array layouts may be displayed to the user to help the user select which TAL is the best. In the illustrated example, clicking on the left column in FIG. 5A for TAL 1 will bring up FIG. 5B, which shows a spatial map of the estimated field power density overlaid on the MRI (in the middle panel 52B) and a graph of the local minimum power density (in the right panel 54B) when the quality of the segmentation is ignored. And clicking on the right column in FIG. 5A for TAL 1 will bring up FIG. 5C, which shows a spatial map of the lowest (or 5% percentile) average estimated TTFields power density overlaid on the MRI (in the middle panel 52C) and a graph of the local minimum power density (in the right panel 54C) when the quality of the segmentation is considered.

While the present invention has been disclosed with reference to certain embodiments, numerous modifications, alterations, and changes to the described embodiments are possible without departing from the sphere and scope of the present invention, as defined in the appended claims. Accordingly, it is intended that the present invention not be limited to the described embodiments, but that it has the full scope defined by the language of the following claims, and equivalents thereof. 

What is claimed is:
 1. A method of determining a quality of a segmentation, the method comprising the steps of: measuring the Dice coefficient between a computed head segmentation and a validated head segmentation of a training set; extracting features to account for a deformable registration quality; incorporating a shortest axis length and a signal to noise ratio of each tissue separately as input image quality; and comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions.
 2. The method of claim 1, wherein the features extracted to account for the deformable registration quality comprise: deformation's field bias, directional variability, and mean per-axis variability.
 3. The method of claim 1, wherein a volume of a shape and a number of connected components are used to describe a segmentation shape.
 4. The method of claim 1, further comprising the step of outputting a result of comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions as a quality score of the computed head segmentation.
 5. The method of claim 1, further comprising the step of outputting a result of comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions as a quality score of the computed head segmentation for an entirety of the input image.
 6. The method of claim 1, further comprising the step of outputting a separate result of comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions as a quality score of the computed head segmentation for each of a plurality of regions within the input image.
 7. The method of claim 1, further comprising the step of outputting a separate result of comparing the measured Dice coefficients between the sets to the Dice coefficients' predictions as a quality score of the computed head segmentation for each of a plurality of tissue types within the input image.
 8. The method of claim 1, wherein the Dice coefficients' predictions are generated using features describing a segmentation shape.
 9. The method of claim 1, wherein the Dice coefficients' predictions are generated using features, each of which is computed per tissue, describing a segmentation shape.
 10. The method of claim 1, wherein the Dice coefficients' predictions are generated using a volume of a segmentation shape and a number of connected components to describe the segmentation shape.
 11. The method of claim 1, wherein the Dice coefficients' predictions are generated using a volume of a segmentation shape and a number of connected components, each of which is computed per tissue, to describe the segmentation shape.
 12. The method of claim 1, wherein the Dice coefficients' predictions are generated using a decision tree regressor.
 13. The method of claim 1, wherein the step of measuring the Dice coefficient generates a measured Dice coefficients per tissue.
 14. The method of claim 1, wherein the Dice coefficients' predictions are generated per tissue.
 15. The method of claim 1, wherein the step of measuring the Dice coefficient is performed between a computed head segmentation and a validated head segmentation of a plurality of training sets.
 16. The method of claim 1, wherein a volume of a shape and a number of connected components, each of which is computed per tissue, are used to describe a segmentation shape. 